Intelligent Middle-Level Game Control

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Researchers

Research units

Abstract

We propose the concept of intelligent middle-level game control, which lies on a continuum of control abstraction levels between the following two dual opposites: 1) high-level control that translates player’s simple commands into complex actions (such as pressing Space key for jumping), and 2) low-level control which simulates real-life complexities by directly manipulating, e.g., joint rotations of the character as it is done in the runner game QWOP. We posit that various novel control abstractions can be explored using recent advances in movement intelligence of game characters. We demonstrate this through design and evaluation of a novel 2-player martial arts game prototype. In this game, each player guides a simulated humanoid character by clicking and dragging body parts. This defines the cost function for an online continuous control algorithm that executes the requested movement. Our control algorithm uses Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in a rolling horizon manner with custom population seeding techniques. Our playtesting data indicates that intelligent middle-level control results in producing novel and innovative gameplay without frustrating interface complexities.

Details

Original languageEnglish
Title of host publicationProceedings of the 2018 IEEE Conference on Computational Intelligence and Games (CIG’18)
Publication statusPublished - 13 Aug 2018
MoE publication typeA4 Article in a conference publication
EventIEEE Conference on Computational Intelligence and Games - Maastricht University, Maastricht, Netherlands
Duration: 14 Aug 201817 Aug 2018
Conference number: 14

Conference

ConferenceIEEE Conference on Computational Intelligence and Games
Abbreviated titleCIG
CountryNetherlands
CityMaastricht
Period14/08/201817/08/2018

    Research areas

  • Continuous control, Game control, Multi-agent systems, Online optimization, Physically-based simulation

Download statistics

No data available

ID: 27511195